Robust working memory in an asynchronously spiking neural network realized in neuromorphic VLSI

Frontiers in Neuroscience

Paolo eDel Giudice¹  Maurizio eMattia²  Vittorio eDante²  Massimiliano eGiulioni²  Patrick eCamilleri³  Jochen eBraun³ 

[1] Istituto Nazionale di Fisica Nucleare;Italian National Institute of Health;Otto-von-Guericke University,;

关键词: working memory;    attractor dynamics;    spiking neurons;    stochastic dynamics;    neuromorphic chips;   

DOI  :  10.3389/fnins.2011.00149

来源: DOAJ

【 摘 要 】

We demonstrate bistable attractor dynamics in a spiking neural network implemented with neuromorphic VLSI hardware. The on-chip network consists of three interacting populations (two excitatory, one inhibitory) of integrate-and-fire (LIF) neurons. One excitatory population is distinguished by strong synaptic self-excitation, which sustains meta-stable states of ‘high’ and ‘low’-firing activity. Depending on the overall excitability, transitions to the ‘high’ state may be evoked by external stimulation, or may occur spontaneously due to random activity fluctuations. In the former case, the ‘high’ state retains a working memory of a stimulus until well after its release. In the latter case, ‘high’ states remain stable for seconds, three orders of magnitude longer than the largest time-scale implemented in the circuitry. Evoked and spontaneous transitions form a continuum and may exhibit a wide range of latencies, depending on the strength of external stimulation and of recurrent synaptic excitation. In addition, we investigated corrupted ‘high’ states comprising neurons of both excitatory populations. Within a basin of attraction, the network dynamics corrects such states and re-establishes the prototypical ‘high’ state. We conclude that, with effective theoretical guidance, full-fledged attractor dynamics can be realized with comparatively small populations of neuromorphic hardware neurons.

【 授权许可】

Unknown